1. Field
The present disclosure relates generally to user profiles and, more specifically, to generating user profiles based on locations identified by matching anonymized user identifiers across differently anonymized data sets.
2. Description of the Related Art
User profiles are useful in a variety of contexts. For example, advertisers often purchase advertising based on a desire to reach potential customers having particular attributes. Such advertisers often employ user profiles to select when, where, or how the advertiser conveys their message. Similarly, market researchers may analyze user profiles to better understand the market for a given good or service based on attributes of buyers of that good or service. In another example, user profiles may be used to customize products or services, for instance, by customizing a software application according to the profile of a user of the software application, or user profiles may be used by governmental agencies to allocate services to geographic areas according to profiles of users in those areas.
User profiles, however, can be difficult to obtain, as users generally have little incentive to generate a profile of themselves for use by others. Such a task can be tedious and unpleasant. Further, users' recollection of their behavior over time can be unreliable.
Instead, advertisers (and other consumers of user profiles) often rely on user profiles generated based on activities of users on various networks or other distributed systems (e.g., cell carriers, ad networks, native applications on smart phones, etc.). Forming such profiles can be difficult, though, because data from individual sources is often insufficient to reliably profile users and the data is often anonymized.
Frequently, available data identifies users uniquely within a given data provider's system, but does not identify users canonically across multiple data provider systems, as each data provider often has a different unique ID for the same user (e.g., a device of a user). This is typically done to comply with privacy policies of the data providers. But as a result, it is difficult to match a record about a user's device from one data provider with a record about the same user's device from another data provider. Also, when users update their equipment, e.g., with a new cell phone, or when a device identifier for a given device is changed by a data provider, it can be difficult to tie a user's existing profile to data from the new equipment, as the third party user identifiers are often based on identifiers of the equipment (e.g., a data-provider-specific hash of a media access control (MAC) address or an advertiser identification number of the device), or to an existing profile mapped to the older identifier.